Agricultural Economics- ICAR JRF/SRF 2024

• Herzberg's Two-Factor Motivation Theory (1959):
• Formulated by psychologist Frederick Herzberg, this dual-factor theory proposes that job satisfaction and job dissatisfaction are driven by two independent sets of factors:
• Hygiene Factors (Extrinsic / Dissatisfiers): Working conditions, salary, job security, company policies, and interpersonal relations. Their presence does not inherently motivate, but their absence causes severe dissatisfaction.
• Motivator Factors (Intrinsic / Satisfiers): Achievement, recognition, challenging work, responsibility, and personal advancement. Their presence actively drives high job performance and deep satisfaction.
• Distractors:
• Abraham Maslow: Hierarchy of Needs (Physiological, Safety, Social, Esteem, Self-Actualization).
• Clayton Alderfer: ERG Theory (Existence, Relatedness, Growth).
• David McClelland: Acquired Needs Theory (Need for Achievement, Affiliation, and Power).

Correct option: (1) Frederick Herzberg

• Security (Safety) Needs in Motivation:
• According to Maslow's hierarchy and contemporary organizational psychology, needs encompassing economic stability, physical protection, job tenure security, health insurance, provident fund protection, and workplace safety fall under Security / Safety Needs (Level 2 of the need hierarchy).
• Distractors:
• Social Needs: Belongingness, affection, friendship, and positive peer affiliation.
• Esteem Needs: Self-respect, status, reputation, professional prestige, and recognition from colleagues.
• Self-Actualization: The highest need level, involving realizing one's fullest personal potential and self-fulfillment.

Correct option: (1) Security needs

• Requirements and Assumptions of Linear Programming (LP):
• Essential structural requirements of an LP model include:

• well-defined Objective Function expressed as a linear equation of decision variables (to be maximized or minimized).
• Linear Constraints (equalities or inequalities) reflecting limited resource availability or operational limits.
• Non-negativity restrictions on decision variables ($X_j \ge 0$).
• Alternative courses of action among which to choose.
  • False Statement: “For each decision variable, there must be one constraint or resource limit.” In linear programming, the number of decision variables ($n$) and the number of constraints ($m$) are completely independent; an LP problem can have dozens of decision variables governed by only two or three resource constraints ($n \gg m$) or vice-versa.

• Artificial Variables and Infeasible Solutions in Linear Programming:
• When solving LP problems with $\ge$ or $=$ constraints using the Big-M Method or the Two-Phase Simplex Method, artificial variables ($A_i$) are added to provide an initial basic feasible solution.
• In Phase I (or during Big-M iterations), the algorithm attempts to drive all artificial variables to zero and out of the basis.
• If the optimality criterion is reached but one or more artificial variables remain in the basis at a strictly positive value ($A_i \gt 0$), it proves mathematically that the constraint system is mutually contradictory and no set of real variables can satisfy all constraints simultaneously, indicating an Infeasible Solution.
• Distractors:
• Unbounded Solution: Occurs when entering variable column has all non-positive entries ($a_{ij} \le 0$), allowing objective value to grow infinitely.
• Multiple Optimal Solutions: Occurs when a non-basic variable has zero net evaluation ($z_j - c_j = 0$) at optimality.
• Degeneracy: Occurs when a basic variable takes a value of zero (tie in minimum ratio test).

Correct option: (2) Infeasible Solution